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openl-tablets

OpenL MCP Server

Official

Run Table

openl_run_table

Run a business-rule table with your JSON input and retrieve its result. This starts Studio's run, waits for completion, and sends progress notifications instead of requiring polling.

Instructions

Execute a regular (non-Test) table with caller-provided JSON and return its result. This is a single high-level operation: it starts Studio's asynchronous run and waits inside the tool call until the result is ready, emitting progress notifications instead of requiring agent-side polling. Pass inputJson as an object keyed by method parameter name or as { params: { parameterName: value }, runtimeContext? }; { params: [...] } is rejected because Studio silently runs it with null arguments. A top-level array is passed as the value of a single array-valued parameter, not as positional arguments. Set withSchema only when the result/parameter JSON Schemas are needed because they can be large. Studio permits one table run per HTTP session, so wait for an active call to finish before starting another. Use openl_start_project_tests and the test-result tools for Test tables. Cancelling the MCP call also cancels and clears the Studio run.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableIdYesTable ID of a regular executable table. Use openl_list_tables() to discover it; use the Tests tools instead for Test tables.
inputJsonYesMethod input as JSON. Use an object keyed by method parameter name, or { params: { parameterName: value }, runtimeContext? }. A top-level array is the value of a single array-valued parameter, not a positional argument list. The value is sent to Studio unchanged.
projectIdYesProject ID returned by backend. Use the exact 'projectId' value from openl_list_projects() response without modification or reformatting.
timeoutMsNoMaximum time for the complete Studio start-and-result workflow, in milliseconds. Default 120000 (2 minutes), maximum 600000 (10 minutes). A timeout cancels the pending Studio run.
fromModuleNoOptional module name whose runtime context should be used. Usually omit; discover module names with openl_list_project_modules().
withSchemaNoInclude result and parameter JSON Schemas. Default false because schemas can be large.
response_formatNoResponse format: 'json' for structured, round-trippable data (default), 'markdown' for human-readable output, 'markdown_concise' for a brief summary (1-2 paragraphs), or 'markdown_detailed' for full details with contextjson
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description discloses the hidden asynchronous start-and-wait behavior, progress notifications, run cancellation when the MCP call is cancelled, and the session-level limitation on concurrent runs. These are behavioral traits not present in the annotations, which only provide openWorldHint.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but every sentence contributes necessary operational context: invocation shape, async behavior, parameter semantics, concurrency limits, and alternatives. It is front-loaded with the core purpose and then addresses edge cases in a logical order.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, 7 parameters, absence of an output schema, and the need to coordinate Studio's async lifecycle, the description covers all critical guidance: input format, timeout behavior, schema inclusion, response format, session limits, and cancellation semantics. No essential calling information is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description goes beyond the schema by explaining the accepted inputJson shapes, why { params: [...] } is rejected, how top-level arrays are interpreted, and when withSchema should be used. This materially reduces the chance of malformed calls.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the operation as executing a regular (non-Test) table with caller-provided JSON and returning the result. It explicitly contrasts with Test-table tools, so an agent can distinguish this from sibling tools without inspecting their schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description states when to use this tool: for a regular table run where the call itself waits for the asynchronous Studio run to finish. It explicitly directs agents to openl_start_project_tests and the test-result tools for Test tables, and it warns about the one-run-per-HTTP-session constraint.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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